Why data curation is the key to achieving deployment-ready AI

Artificial intelligence doesn’t thrive on volume alone. It learns best from purposeful, well-chosen data. According to recent research, models that rely on extensive datasets demonstrate limited historical analysis capabilities, as exhibited by GPT-4 Turbo, which reached a 46% accuracy threshold that barely surpassed random guessing.

What makes the real difference isn’t how much data you have. It’s how strategically that data is selected. That’s where data curation comes in. Data curation is the systematic method of choosing and structuring data to improve its value and significance for AI usage; a vital part of data management and data governance for any organization seeking to do data analysis and train AI.

When organizations select the right data, they turn promising models into effective real-world tools. This blog demonstrates the function of data curation in AI development by explaining that acquiring data alone does not equal successful AI development. It highlights practical techniques to improve model performance through smarter data selection.

What is data curation?

Data curation is the deliberate and thoughtful collection, filtering, and preparation of data for data science, data analytics, and training and evaluating machine learning models. The process is useful in the era of big data, when terabytes of data are collected per day from various devices and sources. The concept comes from library science, where curators manage collections to make information easier to find, understand, and apply. 

Modern data curation goes beyond storage in data warehouses or data cleaning for raw data. This process makes data engineering easy, enabling data transformation as well as data preservation. Data curation is a key quality assurance process, as it identifies high-quality examples aligned with specific task goals, ensuring data integrity. 

This includes filtering out noise, filling in gaps, and shaping the dataset to support better results during model training and evaluation. This process makes data reuse and data discovery, or making specific data more findable, much easier for data scientists and machine learning engineers alike.

Raw data vs curated datasets

Comparing raw data with curated datasets helps illustrate the value of data curation for various data needs. Here's a comparison between raw data and curated datasets:

The main objective is data selection instead of seeking absolute numbers. Andrew Ng describes data-centric AI as “the discipline of systematically engineering the data needed to build a successful AI system.” Enhancing the dataset provides results equivalent to those achieved by improving the algorithm itself.

Why data curation matters in AI development

Data curation is a key component in the development of AI systems. It improves AI model performance and reduces the time and resources needed for development and refinement. Understanding curation's strategic relevance is essential to grasping its importance within the AI training pipeline.

AI training process

The development of AI models occurs through a four-stage systematic pipeline.

• Data collection: Data assets are collected from various sources, such as databases, websites, and vendors. It can also include research data. At this stage, data is raw, inconsistent, and unstructured. Data curation activities improve quality by filtering irrelevant information, ensuring consistent formatting, and organizing data into structured, standardized formats.structured, standardized formats.
• Model training: In this stage, machine learning algorithms are trained to learn relationships within the data. Models learn core features more effectively through curated datasets, which shrink training duration while reducing model overfitting. Insufficient data curation causes models to recognize wrong relationships. 
• Evaluation: After training, the model is evaluated using a validation dataset. It must test edge cases, class imbalances, and rare scenarios robustly.. Using uncurated validation sets creates deceptive accuracy metrics or conceals biases within the system.
• Fine-tuning: Fine-tuning takes a pre-trained model and applies incremental adjustments to its parameters using a smaller, task-specific dataset. This helps the model use existing knowledge while adapting to specialized tasks. Curated datasets are fine-tuned by supplying task-specific data that targets weaknesses and enhances performance.

How data quality directly impacts model performance

Training AI models requires more than just data input into an algorithm. Advanced learning algorithms cannot fix datasets with errors or irrelevant examples. Poor data curation practices result in these outcomes:

• Higher costs: Large, noisy datasets demand high computation and longer training times for results. Data filtering leads to both cost savings and decreased training time.
• Low accuracy: Mislabeled or irrelevant training examples reduce model accuracy. The performance level will stop improving because the model fails to learn from optimal examples.
• Embedded bias: Raw data sets reflect real-world biases. When a facial recognition dataset contains light-skinned faces, the system performs poorly when dealing with darker faces. A lack of proper data curation allows unintentional bias to persist within data, resulting in unsafe operational outcomes.
• Reduced effectiveness: Additional data becomes ineffective for performance improvement once the threshold of maximal accuracy is reached. Curating data selects relevant new information that produces useful results and prevents performance degradation.

Curated data is vital to reaching performance milestones. Increasing accuracy from 90% to 95% usually requires precisely collected data rather than additional general data.

How AI models learn: The role of training data

Understanding how AI models handle data will help us understand the significance of data curation. Modern AI systems operate through various learning-based procedures. Some of them are:

• Supervised learning: The model receives labeled inputs with correct outputs for training purposes, which helps it update its predictive capabilities. If the examples are pertinent and correct, the model learns correctly. If many of them are irrelevant or mislabeled, the model can learn trivial or sometimes wrong patterns.
• Fine-tuning: Many AI models, such as large language models, are initially trained with broad, general data and later fine-tuned on task-specific or domain-targeted datasets. You can optimize the model with selected examples that drive the desired performance.
• Reinforcement learning from human feedback (RLHF): A training process where the model generates responses, which are reviewed by a human evaluator or reward model. The feedback helps the model adjust and prioritize more accurate, useful outputs. RLHF shows that targeted human input can improve model performance beyond what large-scale pre-training achieves.

Feedback loop in AI development

AI development progresses through continuous rounds of these steps:

• Model evaluation: The performance is examined to understand its positive aspects and weaknesses during the model evaluation process.​
• Data curation: Data curation improves the dataset by creating synthetic data that addresses problems found during the evaluation phase.​
• Retraining: After data curation, developers retrain the model to enhance its performance capabilities.​

The process continues until the model enhances its precision and reliability with each iteration.

Case study: Enhancing model performance with targeted synthetic data

An AI lab faced difficulty enhancing a large language model because it was not meeting its benchmarks. At first, the team thought that obtaining 100,000 human-labeled examples would be sufficient to improve their results. The team halted their data collection efforts and studied the points where their model exhibited failures.

The Invisible’s data strategy team revealed that the model succeeded overall but demonstrated two main weaknesses regarding human professional understanding. Using their new understanding, they assembled a compact, handpicked data collection containing 4,000 precisely selected examples focused on areas where the model struggled the most.

The model benchmark results demonstrated a 97% performance increase with just 4% of the initially intended data volume. This case shows that data quantity does not ensure superior AI performance. Specialized and targeted datasets developed to overcome model weaknesses produce better performance outcomes than vast, undirected datasets.

Teams can maximize the value of their data points by pinpointing model weaknesses and delivering the specific correction data needed.

Cutting-edge data curation techniques

Modern AI groups use innovative data curation approaches to make their decisions smarter. Advanced algorithms and human insight surpass standard cleaning procedures to optimize the value of each training example. Let’s explore some of the data curation techniques.

Joint example selection 

Joint example selection is a data selection method designed to meet multiple training objectives at once. The method evaluates candidate examples based on multiple parameters that determine their "learning value" rather than using single selection criteria, such as random sampling.

During execution, the algorithm determines how each data point in the data catalog or data repository will improve model accuracy by combining its relevance score with uniqueness and complexity assessments. The objective is to assemble a collection of examples that provides maximum information to the model.

The Joint Example Selection for Multimodal Learning (JEST) algorithm is a prominent example of joint example selection. Instead of evaluating each example independently, JEST selects data sets in batches based on their combined learning value.

It evaluates the relationships between batch data points during processing, resulting in substantial speedup for training processes. The algorithm delivers performance equal to current advanced models using 13 times fewer iterations and 10 times less computational resources. ​This method helps in:

• Reducing redundancy: The joint example selection technique reduces dataset redundancy by identifying multi-task data points that exclude duplicates.​
• Multifaceted performance issues: This approach locates data examples that enhance performance across multiple dimensions, solving interconnected complex problems between different tasks.

Spectral analysis for data selection

Spectral analysis reveals hidden structures and patterns in data. Converting data into the frequency domain reveals periodic patterns and correlations that are not visible in the original representation. 

Integrating spectral analysis into data selection improves the generalization and robustness of machine learning models. Incorporating these rare examples into training datasets exposes models to a wider range of scenarios, which strengthens their ability to generalize beyond typical cases.

This results in improved performance on edge cases, minimizes overfitting to patterns, and fosters more robust and reliable AI systems. For instance, the SALN (Spectral Analysis and Joint Batch Selection) method uses spectral analysis to prioritize and select samples from each batch, significantly enhancing training efficiency and accuracy.

Bias and error mitigation through curation

The primary objective of data curation is to detect bias and correct systematic errors within the dataset. An unbalanced data distribution, including unequal representation of categories, leads to systematic errors. 

This causes models to perform differently across various groups and conditions. It is essential to review both the dataset composition and model error patterns to reduce bias and modify the data to address unfairness and uncover hidden biases.

Three methods for creating fair training data involve adding more examples of minority categories to the dataset, addressing any class dominance, and identifying cases where models produce incorrect outputs. 

Edge case coverage: Long-tail data selection

Distributing data across various datasets can sometimes leave out specific scenarios or classes, leading to the "long tail" effect. A model may fail in real-world deployment due to limited exposure to specific practical cases. Semantic-guided data augmentation techniques enhance the diversity of rare classes, leading to improved performance on unfamiliar cases.

Curation with a human-in-the-loop system

Human-in-the-loop curation involves applying human strategies and knowledge to scope the data collection and validation processes. It consists of several steps:

• Expert review: Expert reviewers check and authenticate the data. A set of instructors will check if the labels are correct before those images can be included in training the AI.
• Error analysis: Developers and annotators examine what the model did incorrectly to understand the underlying problem or gap. If the AI remains stuck on a specific type of input, that aspect will be dealt with using new or better data until the problem is solved.
• Active learning: Humans label suggestions made by the model. The model continuously learns from new data or data labeled by humans within certain conditions.

As datasets increase, human insight becomes more critical to ensuring quality. There is a large amount of irrelevant or junk content that web scraping or automated data collection might miss, but human curators can ensure the dataset maintains relevance and cleanliness.

Strategic data curation for deployment-ready AI

AI deployments in real-world environments demand superior performance compared to lab testing. Strategic data curation represents the process that develops potential models into ready-to-deploy artificial intelligence systems. The following guidelines explain how to prepare models for production through curative methods.

Generic benchmarks aren’t sufficient

Models that excel on benchmark tests often struggle to achieve comparable scores with actual user inputs. Certain benchmarks can effectively accommodate all potential real-world data distributions. Create customized assessment tests and workflows that reflect your AI system's operational environment while refining the training data to perform well in these evaluations.

When launching a chatbot system, for example, you should collect genuine user questions with complex and unconventional inputs to evaluate the accuracy of its answers. The training process incorporates new data pairs that address your chatbot's performance weaknesses. Through this method, you can enhance real-world scenarios rather than just maximizing benchmark results.

Importance in post-training / fine-tuning stages

After completing the initial training process, the model requires refinements using specialized data sets to improve performance. This process aims to adapt a universal model to a specific domain or application context. Fine-tuning enhances the effectiveness of AI systems by incorporating specialized knowledge that general training lacks.

OpenAI enhanced GPT’s user assistance capacity after a fine-tuning phase that used focused conversational inputs and expert feedback. This was necessary as the original model was not fully prepared for public use. Fine-tuning can improve a pre-trained model and significantly elevate performance from average to exceptional.

Domain-specific data

When dealing with specialized fields, selecting relevant data is an absolute necessity. Standard web data might fail to teach an AI model about technical language and unusual situations in specialized fields. For example, consider these use cases:

• Legal AI: For quality legal results, the model must be trained with preselected documents, including cases, contracts, and legislation reviewed by professionals. Using legal-specific terms in training data helps the AI system perform reliably in legal applications.
• Medical AI: Training medical AI requires an anonymized medical records database, clinical notes, and biomedical texts. Additionally, incorporate doctor-verified samples that illustrate accurate diagnoses and treatments. Insights from this specialized data will ensure safe, accurate model results.
• Coding AI: It uses a curated set of documented code, questions from forums, and accurate programming examples. After eliminating security risks and bugs in its training examples, the AI evolves into a superior coding assistant, resulting in reliable code output.

Selecting training data ensures model readiness for specific domains. Deployment involves testing system performance with real data, followed by improvements through retraining or fine-tuning. Each cycle enhances your dataset's alignment with real-world conditions, increasing model reliability.

Ensure high quality data curation with Invisible

Selecting appropriate training data for an AI model requires more than just simple cleaning or a labeling method. Data curation serves three vital functions: prioritizing quality data over excess data, recognizing edge cases, and reducing bias while facilitating ongoing improvement.  

The strategic process of selecting appropriate data has become a core competitive element in contemporary AI development. Organizations that maintain sustained data curation processes achieve superior results than those that merely gather data without strategic intent. Properly curated data helps premature AI solutions evolve into high-performing real-world AI systems.

Start with a custom evaluation of your AI model, ideally conducted by a team of domain experts who can identify blind spots, biases, and edge case failures. Use these insights to develop a targeted strategy that addresses high-impact examples and fixes missing areas. A trusted partner like Invisible helps accelerate deployment and unlock stronger model performance.